Driver Dojo is an autonomous driving environment for benchmarking reinforcement learning (RL) methods based on the popular Simulation of Urban Mobility (SUMO) platform. It focuses on generalizability, meaning that we aim to provide means to develop, prototype, research and compare RL methods that are able to handle broad distributions of driving scenarios. For this, we provide randomization on the street network level, the task level (ego routing), and traffic level, where the latter encompasses traffic density, traffic initialization and routing, as well as driving personalities through sampling of unique physical, car-following, lane-change and junction model parameters on a per-vehicle basis.
The key features of Driver Dojo are:
- Generalization environments: Five fine-tuned environments for intersection, roundabout and highway driving scenarios.
- Pre-implemented observations: Ten composable observers allowing for compact, ground-truth numerical observations extracted from the SUMO simulation state as well as image observations through a simplified birds-eye observer and co-simulation in Carla.
- Multiple action spaces: A continuous, discretized as well as semantic action space founded on automatic lane-following through a low-level controller.
- Different vehicle models: Physical dynamics and simplified lane-following car models.
- Script library: Python scripts for e.g. manually driving an environment using keyboard inputs, map plotting and RL model benchmarking through Tianshou and Hydra.
- High Configurability: Fully typed configuration capabilities from the outside, allowing for diverse driving scenario creation without having to touch any source-code.
- Full SUMO support: Any configuration or static scenario definition natively supported in SUMO is straightforward to integrate as an RL environment.
- Clean and performant: Our codebase is modular, clean and fully documented. We are able to train an PPO agent for 10 million time steps in less than 24 hours on our desktop workstation. Have a look at our documentation!
Something for the eye.
Have a look into this folder for more videos.
This scenario was built just using SUMO's netedit tool.
Here, we imported an area around our Fraunhofer institute in Nuremberg from OSM using SUMO's tooling scripts and added custom traffic definitions using netedit.
Installing Driver Dojo and its requirements is very easy and does not require any source compilation whatsoever.
Note that Carla does not work on macOS in its current state. However, if you do not plan to use the co-simulation feature you can nevertheless use our environment on macOS.
-
Install SUMO 1.13 located here.
-
Install Carla 0.9.13 located here.
-
Install our Driver Dojo package via:
git clone https://github.com/seawee1/driver-dojo.git
cd driver-dojo
pip install -e .or
pip install -e .[benchmark]if you plan to use our benchmark scripts.
- As a last step, place the SUMO tooling scripts found inside the official repository under
driver-dojo/tools, i.e.:
git clone https://github.com/eclipse/sumo.git
cp -r sumo/tools/* driver-dojo/tools/The easiest way to test your installation is to run:
python -m driver_dojo.examples.performance_benchmark [env=...] [subconfig.value=...]where you can additionally pass the name of the environment you would like to test out via env=... and as many configuration parameters as you would like, e.g. simulation.dt=0.1. Have a look at the config.py to get an overview.
To get a real impression of what our benchmark looks like, please run:
python -m driver_dojo.examples.manual_control [env=...] [subconfig.value=...]which lets you drive the ego vehicle using your keyboard.
Our environments follow the OpenAI Gym interface. To use it as a training environment, follow the typical workflow, i.e.
import gym
import driver_dojo
env = gym.make("DriverDojo/PlaceEnvNameHere-v0")
obs = env.reset()
...Generally, environment names are structured as DriverDojo/{Action Space}-{Vehicle Model}-{Scenario}-v0, e.g. DriverDojo/Sem-God-Intersection-v0 or DriverDojo/Disc-KS-Roundabout-v0.
You can pass custom configurations through a config dict, for example:
import gym
import driver_dojo
config = dict(
simulation=dict(
dt=0.05
),
observations=dict(
observers=[...]
)
)
env = gym.make("DriverDojo/PlaceEnvNameHere-v0", config=config)We use Tianshou and Hydra for our benchmark code base. Run:
python -m driver_dojo.examples.benchmark hydra.job.chdir=True algo=ppo_numeric obs=default env="DriverDojo/Sem-God-Intersection-v0"to train PPO on the intersection environment. This will create a subfolder inside output in which Tensorboard logs and model checkpoints will be placed.
Similarly, as shown above, this allows us to overwrite every configurable parameter from the outside, e.g. ++env_train.simulation.dt=0.05 will change the simulation time resolution of the training environment to 50ms.
For the results of our paper, we used env_train=train_x_0 and env_train=train_x_1, where x is the number of scenario constellations used for the respective experiment. For seeding of the RL method, we used seeds 1337 and 1338.
Besides SUMO and Carla, we thank the authors of Hydra, Tianshou, scenariogeneration and commonroad-vehicle-models, without which this project in its current form would not have been possible.
If you use Driver Dojo in your own work, please cite:
@misc{https://doi.org/10.48550/arxiv.2207.11432,
doi = {10.48550/ARXIV.2207.11432},
url = {https://arxiv.org/abs/2207.11432},
author = {Rietsch, Sebastian and Huang, Shih-Yuan and Kontes, Georgios and Plinge, Axel and Mutschler, Christopher},
title = {Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving},
publisher = {arXiv},
year = {2022},
}This project is licensed under the License for Academic Use of Fraunhofer Software, Version 2.0.
For licenses of source code of third-party Open-Source projects directly included inside our repository, have a look at the 3rd-party-licences.md file.